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Blind Document Image Quality Prediction Based on Modification of Quality Aware Clustering Method Integrating a Patch Selection Strategy

Abstract : The quality of document images has direct impacts on the performance of document image processing systems. Document Image Quality Assessment (DIQA) is, therefore, of fundamental importance to a numerous document processing applications. As manual quality assessment is almost impossible for a huge volume of document images generated in day-today life, it is critical to develop intelligent machine operated methods to estimate the quality of document images. In this paper, a blind document image quality assessment method is proposed to deal with the problem of DIQA in real scenarios, as reference images are not always available. To estimate the quality of a document image, the document is rst sampled into a set of patches. The extracted patches are then ltered out based on their level of foreground information using a patch selection strategy. For every selected patch, a cluster assignment is then performed to obtain its quality from a quality aware bag of visual words constructed using k-means clustering. An average pooling is nally employed to estimate the quality of the input document image. To evaluate the proposed method, a dataset composed of document images and three scene image datasets were considered for experimentation. The results obtained from the proposed method demonstrate the eectiveness of the proposed DIQA method. These achievements in applied computational intelligence, expert and decision support systems make a good foundation for creating practical tools to automate document image forgery detection, and archiving process.
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Submitted on : Tuesday, May 15, 2018 - 11:26:46 AM
Last modification on : Tuesday, January 11, 2022 - 5:56:21 PM
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Alireza Alaei, Donatello Conte, Maxime Martineau, Romain Raveaux. Blind Document Image Quality Prediction Based on Modification of Quality Aware Clustering Method Integrating a Patch Selection Strategy. Expert Systems with Applications, Elsevier, 2018, ⟨10.1016/j.eswa.2018.05.007⟩. ⟨hal-01792116⟩



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